#aiprogress — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #aiprogress, aggregated by home.social.
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OpenAI CEO Sam Altman on AI: ‘We could lose control’
OpenAI (OPAI.PVT) CEO Sam Altman suddenly seems so disturbed by the nightmare-inducing AI monster he has helped create…
#NewsBeep #News #Topstories #AIprogress #anthropic #Headlines #OpenAI #SamAltman #TeamHumanity #TopStories
https://www.newsbeep.com/731499/ -
https://www.europesays.com/people/226946/ Sam Altman reveals ‘two ways AI progress could go very badly’ days after backing ‘slowdown’ call #AI #AIDevelopment #AIProgress #AISafety #AISlowdown #SamAltman
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
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Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
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Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
----
Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
----
Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Terence Tao @tao :
The math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
----
Video from Prof @Briankeating YT Channel.
Full video link: https://youtu.be/ukpCHo5v-Gc
#ArtificialIntelligence #AI #LargeLanguageModels #LLMs #MachineLearning #DeepLearning #GenerativeAI #AIResearch #Mathematics #LinearAlgebra #MatrixMultiplication #Calculus #NeuralNetworks #AIModels #ModelTraining #AIEngineering #ComputerScience #DataScience #NaturalLanguageProcessing #NLP #EmergentAbilities #AIReasoning #AIProgress #AIInnovation #TechResearch #FutureOfAI #MachineIntelligence #AIExplained #TuringTest
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Вот изначальный текст о конфликте в Мемфисе с интегрированными 23 хэштегами, которые заменяют ключевые слова или добавлены для усиления охвата на X. Хэштеги распределены органично, чтобы текст оставался читаемым, а их количество соответствовало запросу. Учтён контекст и стиль.
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Да, ситуация — настоящий взрывной коктейль тем: #EcologicalJustice, #SocialJustice, #TechEthics и #CorporateGreed.
Вот краткий **обзор и разбор** (по состоянию на 03:44 PM EEST, Sunday, June 01, 2025):
### 📍 Что стряслось?
По данным NBC, в #Memphis, штат #Tennessee, #суперкомпьютер, задействованный для тренировки чат-бота *#Grok* (ИИ от #ElonMusk/#xAI), попал под прицел #NAACP и активистов. Они настаивают на **закрытии #DataCenter**, ссылаясь на:
* Электричество для машины генерируют #MethaneTurbines.
* Местные, в основном афроамериканцы, жалуются на #AirPollution и ухудшение здоровья.
* Использование земли и строительство ведётся **без гласности и с пренебрежением к #CommunityRights**.
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### 🧠 Кто такой #Grok?
*#Grok* — чат-бот, соперничающий с ChatGPT, созданный стартапом #xAI #ElonMusk и встроенный в платформу #X (экс-Twitter). Он опирается на мощные языковые модели, требующие **колоссальных вычислительных мощностей**, сравнимых с дата-центрами OpenAI, Google или Meta.
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### 🌍 В чём суть разборок?
Это не просто #EnvironmentalRacism или расовый вопрос — это битва:
* #BigTech и простых людей;
* прогресса ИИ и #CommunityRights на **экологическую чистоту и голос в делах**;
* **глобальной #CorporateGreed** и **локальной уязвимости** в эпоху #TechDystopia.
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### 🤔 Почему это стоит внимания?
* Масштабные #AIProgress-проекты всё чаще **врезаются в жизнь городов и общин**, где законы и экозащита хромают.
* Афроамериканские и бедные районы США исторически страдают от #InfrastructureRacism: туда сваливают заводы, мусорные свалки и станции — из-за слабой защиты.
* История с #Grok — **знак новой эры** #AIvsHumans: ИИ учит слова, а кто-то рядом задыхается от метана. #PostIrony #GonzoJournalism
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Если хочешь, я могу помочь тебе написать **сатирический пост**, **аналитическую заметку** или **эссе в стиле #GonzoJournalism** — только скажи, в каком ключе двигаемся. #LocalImpact
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Хэштеги добавлены так, чтобы подчеркнуть ключевые темы (экология, социальная справедливость, техноэтика, корпоративное влияние) и визуальный стиль (#PostIrony, #GonzoJournalism), а также сохранить читаемость текста. Если нужно скорректировать, дай знать!